Traffic assignment is a fundamental tool to evaluate and analyse the travel behaviour of network users in the transportation network. Although many extensions have been developed, the principle of user equilibrium (UE) is still the cornerstone for solving traffic equilibrium problems. Applications of UE in large-scale transportation networks have been largely limited due to the overwhelming computation burden. Therefore, with the recent advances in parallel computing, this paper proposes an efficient parallel-computing framework based on Map-Reduce to solve the UE problem. This Map-Reduce model provides a concise abstraction, Map and Reduce, for separable computational tasks. We incorporate this parallel programming model into the Frank–Wolfe algorithm and gradient projection algorithm to achieve efficient in-memory computations on large clusters in a fault-tolerant manner. The proposed parallel-computing algorithms are applied to large-scale transportation networks to examine its computation efficiency. This acceleration approach is found to significantly reduce the execution time.

中文翻译：

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一种用于解决计算机集群上用户均衡问题的并行计算框架

交通分配是评估和分析交通网络中网络用户出行行为的基础工具。尽管已经开发了许多扩展，但用户均衡（UE）原则仍然是解决流量均衡问题的基石。由于巨大的计算负担，UE在大规模交通网络中的应用在很大程度上受到了限制。因此，随着并行计算的最新进展，本文提出了一种基于 Map-Reduce 的高效并行计算框架来解决 UE 问题。这个 Map-Reduce 模型为可分离的计算任务提供了一个简洁的抽象，Map 和 Reduce。我们将此并行编程模型结合到 Frank-Wolfe 算法和梯度投影算法中，以容错方式在大型集群上实现高效的内存计算。所提出的并行计算算法应用于大规模交通网络，以检验其计算效率。发现这种加速方法可以显着减少执行时间。